Resting-state electroencephalogram microstate to evaluate post-stroke rehabilitation and associate with clinical scales

Zhongpeng Wang; Zhaoyang Liu; Long Chen; Shuang Liu; Minpeng Xu; Feng He; Dong Ming

doi:10.3389/fnins.2022.1032696

Resting-state electroencephalogram microstate to evaluate post-stroke rehabilitation and associate with clinical scales

Front Neurosci. 2022 Nov 18:16:1032696. doi: 10.3389/fnins.2022.1032696. eCollection 2022.

Authors

Zhongpeng Wang^{1

2}, Zhaoyang Liu¹, Long Chen², Shuang Liu², Minpeng Xu^{1

2

3}, Feng He^{1

2

3}, Dong Ming^{1

2

3}

Affiliations

¹ Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
² Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
³ Tianjin International Joint Research Center for Neural Engineering, Tianjin, China.

Abstract

Introduction: Stroke is usually accompanied by a range of complications, like post-stroke motor disorders. So far, its evaluation of motor function is developed on clinical scales, such as Fugl-Meyer Assessment (FMA), Instrumental Activities of Daily Living (IADL), etc. These scale results from behavior and kinematic assessment are inevitably influenced by subjective factors, like the experience of patients and doctors, lacking neurological correlations and evidence.

Methods: This paper applied a microstate model based on modified k-means clustering to analyze 64-channel electroencephalogram (EEG) from 12 stroke patients and 12 healthy volunteers, respectively, to explore the feasibility of applying microstate analysis to stroke patients. We aimed at finding some possible differences between stroke and healthy individuals in resting-state EEG microstate features. We further explored the correlations between EEG microstate features and scales within the stroke group.

Results and discussion: By statistical analysis, we obtained significant differences in EEG microstate features between the stroke and healthy groups and significant correlations between microstate features and scales within the stroke group. These results might provide some neurological evidence and correlations in the perspective of EEG microstate analysis for post-stroke rehabilitation and evaluation of motor disorders. Our work suggests that microstate analysis of resting-state EEG is a promising method to assist clinical and assessment applications.

Keywords: clinical scales; microstate analysis; post-stroke; rehabilitation assessment; resting-state EEG.